We hit this wall constantly at Databricks. Our production agents, from Genie to Agent Bricks, run into the same context limitations at some point. Genie provides a clean example: a single query searches a customer's entire workspace, calling many tools to pull data from tables, vector indices, and dashboards. To address this, we built a MemEx of our own, and validated it inside multiple production and internal agents.

On hard enterprise structured retrieval tasks, Figure 1 shows that MemEx pushes the cost-vs-accuracy frontier for every model. Frontier models like Opus 4.6 and Sonnet 4.6 gain 2–5 percentage points at 25–30% less token cost. Open-weights models like Qwen3.5-122B (18% → 36%) and Qwen3.5-397B (20% → 38%) nearly double their accuracy at 40–50% less token cost. Because MemEx can operate over arbitrarily long inputs, it also unlocks two further applications: auditing agent trajectories, including MemEx's own, that wouldn’t normally fit in a single context window and parallel thinking across multiple trajectories.

MemEx gives the LLM a programmable scratchpad: a typed Python kernel that holds tool outputs, transforms them with code, and materializes only the print statements as tokens in the context. Inside this environment, the rollout becomes a self-extending Python program. During each turn, the agent authors a new block, the kernel keeps state alive, and the next block builds on what came before. Tools are exposed as typed Python functions with typed parameters and typed return values. Tool outputs land as Python objects in MemEx's scope, where they persist across turns. The agent composes them with code, defines helper functions when a pattern repeats, and spawns sub-agents as asynchronous function calls over the same scope.

MemEx is in the code-as-action family introduced by CodeAct (Wang et al., 2024), with production variants in Anthropic's Programmatic Tool Calling and Cloudflare Code Mode. MemEx sets itself apart by dropping into an existing ReAct (Yao et al., 2022)-style agentic framework, with persistent scope, sub-agent primitives, and typed returns wired in. Together, these unlock capabilities the JSON/XML tool calling paradigm lacks: